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Abstract
Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.
Highlights
Physical inactivity is the fourth leading cause of global mortality
We estimated metabolic energy expenditure with several methods including the Heart Rate Model, the Activity Monitor, the Musculoskeletal Model using muscle-level energy estimates, the Data-Driven Model using all wearable sensor data segmented by stride in a linear regression model (Supplementary Fig. 2), Per-Breath Respirometry, and FastEstimated Respirometry which fit laboratory-based respirometry measurements to a first-order exponential function for quicker steady-state estimates
The approach used in the Wearable System meets the requirements for devices that monitor physical activity on a large scale, Fig. 2 Estimating energy expenditure during steady-state conditions. a Methods estimating energy expenditure were evaluated during 6-minute steadystate conditions followed by 3 mins of quiet standing
Summary
Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. We present a Wearable System that estimates metabolic energy expenditure in real-time during common steadystate and time-varying activities with substantially lower error than state-of-the-art methods. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring. Health policy committees have requested a tool to objectively monitor physical activity at a large scale using a metric like metabolic energy expenditure[2]. Estimating daily active energy expenditure requires monitoring common and high-expenditure activities such as walking, stair climbing, running, and biking. Design guidelines suggest that wearable medical devices should be low-cost, easy to don and doff, and allow normal motion[10]
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